Presentació feta pel Dr. Jaume Barceló i Dra. Lidia Montero el 21/01/2015 a la jornada de transferència digital, UPC.

1. SmartMobility
Enero 2015
Responsable Transporte y TIC
Dr. Jaume Barceló
Jaume.barcelo@upc.edu
Dra. Lidia Montero
Lidia.montero@upc.edu

2. 2
TRANSPORT & SMART MOBILITY
EXPERTISE AND RESEARCH INTERESTS

3. • Public Transportation Planning
• Development of Real-Time Adaptive traffic Control
Systems
• Development and testing of Mathematical Models and
Optimisation Algorithms for Transportation Planning
• Development, implementation and testing of
Microscopic and Mesoscopic traffic simulators.
• Urban logistics
• Real-time fleet management
Experience in applying optimization and
simulation models to transportation problems

4. • New generation traffic and travel forecasting
models
• Real-time multimodal personal journey planners
• Urban logistics
• Real-time Fleet Management
• Emergency and disasters management
• Agent based simulation
• Rapid Prototyping for urban design
ICT & Transports Research interests

5. Microscopic and Mesoscopic traffic simulation
Founders of TSS (AIMSUN microscopic & mesoscopic traffic simulation)

6. 6
o
Loop detectors /
Magnetometers
Vehicle n
Reaches RSU p
At time t3
Vehicle n
Sends AVL message
At time t0+t
Vehicle n
Reaches RSU k
At time t1
Vehicle n
Reaches RSU m
At time t2
Vehicle n
Sends AVL message
At time t0+2t
i
Vehicle n
Leaves origin i
At time t0
RSU-IDy
On-board unit of equipped vehicle n
captured by RSU-IDx at time t1
On-board unit of equipped vehicle n re-
captured by RSU-IDy at time t2
Data (RSU Id, mobile
device identity, time
stamp ti) sent by GPRS
to a Central Server
RSU-IDx
Data (RSU Id, mobile
device identity, time
stamp) sent by GPRS to
a Central Server
AVL Equipped vehicle sends message
(id, position, speed) at time t
V2V exchange
𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆 𝑹𝑺𝑼𝒙 − 𝑹𝑺𝑼𝒚
𝒕 𝟐 − 𝒕 𝟏
Average speed
Smart City Sensored City
Multi-technological data sources

7. Traffic Data Analytics
We are working on most of the services required for Smart
Mobility or for dealing with traffic from the perspective of
analytics, including data filtering, completion and fusion,
the interoperability of data and the processing of huge
amounts of data, or Big Data.
Statistical & traffic
flow based models to
identify and eliminate
the outlier
observations
Missing data
supply
Procedures of space-
time traffic state
reconstruction from
heterogeneous data
sources

8. • Combine traditional traffic supervision technologies with the
latest available or soon to be available ICT.
• Data Filtering, Merging and Completion Module.
– Filtering of data, integration of new types of data that had not traditionally been
used in traffic information systems, especially those that allow real-time treatment
of information.
– Development of completion models for missing data coming from the ICTs.
– Development of data merging models that combine large amounts of data sources
unprecedented in the field of traffic.
Avanza Competitividad R&D (2010-2012)
Program
http://inlab.fib.upc.edu/en/in4mo-advance-information-system-mobility-people-
and-vehicles
In4Mo. Advance Information System for the
Mobility of People and Vehicles

9. • Turning citizens into
an active agent in
the generation of
mobility
data using mobile
devices
• Probe Person Survey
methodology
• Analysis of mobility
and urban
behaviour
9
http://inlab.fib.upc.edu/en/probe-person-survey-upcnet
Electronic Data Collection for Activity Based
Demand Modeling: Probe Person Survey

10. Source: Electronic Instrument Design and User Interfaces for Activity Based Modeling (Hato & Timmermann - 2008)
Electronic Data Collection for Activity Based
Demand Modeling: Probe Person Survey

11. – Vehicle Routing Problem Algorithms
• Time-dependent
• Stochastic Demand
– Micro/Meso Traffic Simulation
Real-time Fleet Management
Fleet Management Center Solution

12. Decision Support System based on the Macro Fundamental Diagram and, through the
proper processing of the data from all detectors, allows to identify on real time the
present traffic state of a urban area and its evolution. This information is the used, in
combination with traffic models, for the implementation of proactive traffic control
strategies.
Decision support systems:
Traffic Management
Figure 6 Potential use of the Network Fundamental Diagram to support Active Area WideTraffic Management
Strategies
URBAN AREA TO MANAGE
LARGE URBAN OR METROPOLITAN AREA
Origin r
Destination s
Congestion
Alternative
recommended
route
GATE-OUT
GATE-IN
QUEUE
Estimation algorithm for 𝒏 𝒌
ADAPTIVE FLOW CONTROL STRATEGY
A
B
Critical Point in
the managed area
Allow access Restrict access
C
Real-time
Traffic Data
Measurements
from sensors
Output flows
n(k-1)
Input flow
rates (k)

13. Point to point instant dynamic ridesharing.
• A pilot test planned in a city in the Barcelona
metropolitan area to share private vehicles to go
to the train station located in the closest city.
Users can demand the transport just a few
minutes in advance.
• It uses mobile technology and a tracking server.
The main challenges of this project are not
technological but related to social and security
issues.
More information: http://inlab.fib.upc.edu/en/dynamic-ridesharing
Dynamic ridesharing

14. It is not possible to install sensors in ALL streets.
It is necessary to look for different ways.
In the same way as weather, Traffic may be
calculated and predicted from a limited number
of sensors through the use of models
Smart mobility – Traffic forecasting
Example: Weather forecast The model makes use of a small set of data and
provides us with detailed information
Even more: the model can predict the future
evolution of weather conditions
Meteorological
model

15. New generation forecasting models for high-quality traffic and
travel information, short-time real-time predictions
• Current available models and services are useful to provide
information for long-term traffic planning or they provide
information only based on past information.
• New generation forecasting models are required to provide high-
quality traffic and travel information, specially for short term
predictions used to plan a trip.
Example of applications/projects:
• Electric vehicle trip planning
• Real-time multimodal trip planning, combining different transport
modes
Smart mobility: Traffic and travel forecasting

16. Mesoscopic
Traffic
Simulation
Models and
the
Information
they supply
for
Management
Network
Model
Time-dependent
OD matrices
Traffic
Control Data
calculate path flows
at time t
Perform Dynamic
Network Loading
(traffic simulation)
Initial path calculation
and selection
Estimate path travel
times at time t
DUE Convergence criteria
(Rgap ) satisfied
YES
STOP
NO
Estimate the new path sets
according to the computational
algorithm for equilibrium (MSA,
Projection…) adding new paths
or removing existing ones for
each OD pair and time interval
MAIN OUTPUTS
- Time dependent flows
- Time dependent travel times
- Queue dynamics
- Congestion dynamics
Velocidad en los arcos Tiempo de viaje de los arcosLink Speed Map Link Travel Times
Alternative paths and
forecasted path travel times
COMPLETE NETWORK INFORMATION

17. P&R
P&R
Interactive, integrated, multimodal, real-time
decision support system (pre-trip, in-trip)
Data interoperability
Real-Time Advanced Journey Planner

18. Tool/Method to support agile low cost urban design decisions.
Used to optimize the location of elements such as traffic sensors,
eVehicle charging points, accessibility analysis or location of
emergency services, etc.
It answers the questions:
• How many?
• Where to locate them?
Based on research on Location Problems.
Rapid Prototyping for urban design

19. http://inlab.fib.upc.edu
inlab@fib.upc.edu
+34 93 401 69 41
c/ Jordi Girona 1-3
Campus Nord. Edifici B6
08034 Barcelona
Spain
Twitter: @inLabFIB
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